Digital cameras have become commonplace. The digital image data produced by digital cameras can be analyzed and processed by digital image processing techniques. Conventional digital image processing algorithms range from basic image “improvement” processing to optimize exposure, color saturation, and/or sharpness in the image, to the wholesale alteration of image subjects and of the image itself.
In digital image processing, the operation of semantic segmentation refers to the analysis of a digital image to identify features or subjects in the image. In a general sense, semantic segmentation assigns each pixel of an image to a predefined feature or classification. For example, in an image of dogs and cats, semantic segmentation may assign the pixels of each dog in the image to the category “dog,” and assign the pixels of each cat in the image to the category “cat.”
Semantic segmentation processing may be used in a wide variety of applications (medical imaging, robotics, etc.) to classify the features of a captured image. For example, semantic segmentation may be used in an advanced driver assistance system (ADAS) or autonomous vehicle to process images of the environment in which a vehicle is operating and identify the various features (e.g., road, vehicles, people, etc.) present in the environment.